README.md

---
pretty_name: Code Space Complexity Tasks
configs:
- config_name: code_from_sc_mcq
  data_files:
  - split: train
    path: data/code_from_sc_mcq-*.parquet
- config_name: code_to_sc_mcq
  data_files:
  - split: train
    path: data/code_to_sc_mcq-*.parquet
- config_name: sc_direct
  data_files:
  - split: train
    path: data/sc_direct-*.parquet
- config_name: default
  data_files:
  - split: train
    path: data/*.parquet
size_categories:
- 100K<n<1M
dataset_info:
  features:
  - name: data_source
    dtype: string
  - name: prompt
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: ability
    dtype: string
  - name: reward_model
    struct:
    - name: extraction_method
      dtype: string
    - name: ground_truth
      dtype: string
    - name: style
      dtype: string
  - name: extra_info
    struct:
    - name: comp
      dtype: string
    - name: idx
      dtype: string
    - name: problem_id
      dtype: string
    - name: task_type
      dtype: string
  download_size: 482841586
  dataset_size: 493521479
---

## Code Space Complexity prediction
Sourced from BigOBench, using a subset of the N-most commonly occuring space complexities in the dataset

Three tasks:
- sc_direct: Directly predict the space complexity as O(X) given the code
- code_to_sc_mcq: Select the correct space complexity from four options, given the code
- code_from_sc_mcq: Given a space complexity, select the code with that space complexity from four options.


|Complexity|
|:---|
|O(1)|
|O(logn)|
|O(logn*logm)|
|O(n)|
|O(n+m)|
|O(nlogn)|
|O(n*m)|
|O(n**2)|